This application is based on and claims priority from Korean Patent Application No. 10-2003-0059919 filed on Aug. 28, 2003 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.
1. Field of the Invention
The present invention relates to a method and system for recommending content, and more particularly, to a method and system for rating content using a user's profile and recommending the highest-ranking content to the user.
2. Description of the Related Art
With the advent of digital TVs, it has become possible for users to enjoy unprecedentedly diversified multimedia contents. Along with the huge amount of available information, it has become an increasing challenge for the users to find the location where relevant contents reside in a timely way. Since usability of contents provided by a service provider is determined whether it is relevant to the end user's information needs, numerous information systems and applications have been developed for content customization. As one of those developed applications, a recommendation technology is implemented in the form of a centralized system such as an Internet-based personal electronic program guide (EPG) service, or a distributed environment such as a digital TV and a set-top box.
Much research and effort into content recommendation technologies has been conducted and a variety of techniques have been proposed. Most existing systems rely on several approaches including user profiling, content rating activity, and so on.
User profiling is employed for the purpose of creating profiles based on user preference data, which may gradually change over time according to users' changeable interests and favorites. A conventional user profiling approach has employed explicit feedback or explicit preference items provided by a content user or implicitly observed the characteristics of user behavior to the content. In order to set explicit preference items, a user needs to input item values corresponding to detailed user preferences of various fields, which is time-consuming work and requires elaborate efforts, degrading system effectiveness. Conversely, extraction of implicit preference items eliminates user troubles of having to directly define preference items. In other words, users do not have to input information on genres of great interest, favorite movie actors or actresses, their ages, etc. This implicit profiling allows continuous monitoring without user intervention, thus providing information on user preferences continuously for a long period of time.
However, known systems present a problem in that they analyze some limited aspects of a user's behavior. For example, only selection for playing specific content or which channel the user tunes to is usually taken into consideration. An improved system only uses the temporal characteristics of a user's behavior such as playtime as material for discovering the user's preferences. Here, the playtime may not be identical to actual viewing time. When the behavior being observed is limited in scope in this way, a user profile degrades in quality. Thus, to measure user preferences, there is a need for a model having a more complicated structure, that is, various parameters.
Meanwhile, the content rating process is performed to measure the possibility of user satisfaction with specific content. Unlike user profiling, this approach provides users with a limited list of high-ranking contents, which is often called custom electronic program guide. Existing systems have rated priority of contents by keyword or association-based associativity measurement, unlike in the user profiling module. Although the keyword-based approach is effective for text filtering, it may not be useful enough to be applied for audiovisual contents because it cannot be accompanied by comprehensive and full text description.
The association-based approach is based on the existence of a predefined content-type hierarchical structure (e.g. drama, news, sports, music, etc.). This approach is used to measure a degree of content appropriateness when the type of content does not match a user profile. In the conventional association-based approach, associativity between contents is measured using either a depth or path length. In the former case, two different types of contents are compared with each other by measuring each depth relative to the common uppermost type node in the content-type hierarchical structure. In the latter case, the number of links between nodes in a semantic network is counted. While the depth-based method tends to overlook the actual distance between nodes that will undergo measurement of associativity, a path length-based method tends not to consider the depths of the nodes, i.e., their degrees of generality. In addition, since there are an unlimited number of content-type hierarchical structures available that are organized according different rules, there is a need for a method for measuring associativity more flexibly that can be easily applied to differently organized hierarchical structures.